




import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


snake_case_  :      Optional[int]		=		16
snake_case_  :      Optional[Any]		=		32



def        __a							(  __UpperCAmelCase		:						Union[str, Any]      ) ->					Union[str, Any]:
							"""simple docstring"""




							return int(x / 2**20      )





class 					snake_case_   :



				'''simple docstring'''





				def __enter__(      self							:   Optional[Any]							)							->			str:
											gc.collect()
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
											lowerCamelCase_			:    Tuple   						=      torch.cuda.memory_allocated()
											return self





				def __exit__(      self							:   Any      ,							*__magic_name__							:   str							)							->			Tuple:
											gc.collect()
											torch.cuda.empty_cache()
											lowerCamelCase_			:    Optional[Any]   						=      torch.cuda.memory_allocated()
											lowerCamelCase_			:    Tuple   						=      torch.cuda.max_memory_allocated()
											lowerCamelCase_			:    Tuple   						=      bamb(self.end - self.begin							)
											lowerCamelCase_			:    List[Any]   						=      bamb(self.peak - self.begin							)
											# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")



def        __a							(  __UpperCAmelCase		:						Accelerator   ,		__UpperCAmelCase		:						int = 16   ,		__UpperCAmelCase		:						str = "bert-base-cased"   ,		__UpperCAmelCase		:						int = 320   ,		__UpperCAmelCase		:						int = 160   ,		) ->					Optional[int]:
							"""simple docstring"""




							lowerCamelCase_			:    Union[str, Any]   						=      AutoTokenizer.from_pretrained(__UpperCAmelCase      )
							lowerCamelCase_			:    Union[str, Any]   						=      load_dataset(
							    "glue"   ,		"mrpc"   ,		split={"train": f"train[:{n_train}]", "validation": f"validation[:{n_val}]"}      )

							def tokenize_function(__UpperCAmelCase		:						List[str]      ):
														# max_length=None => use the model max length (it's actually the default)
														lowerCamelCase_			:    Optional[Any]   						=      tokenizer(examples["sentence1"]   ,		examples["sentence2"]   ,		truncation=__UpperCAmelCase   ,		max_length=__UpperCAmelCase      )
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							lowerCamelCase_			:    Dict   						=      datasets.map(
							    __UpperCAmelCase   ,		batched=__UpperCAmelCase   ,		remove_columns=["idx", "sentence1", "sentence2"]   ,		load_from_cache_file=__UpperCAmelCase      )

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							lowerCamelCase_			:    Tuple   						=      tokenized_datasets.rename_column("label"   ,		"labels"      )

							def collate_fn(__UpperCAmelCase		:						Any      ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(__UpperCAmelCase   ,		padding="max_length"   ,		max_length=128   ,		return_tensors="pt"      )
														return tokenizer.pad(__UpperCAmelCase   ,		padding="longest"   ,		return_tensors="pt"      )

							# Instantiate dataloaders.
							lowerCamelCase_			:    Optional[int]   						=      DataLoader(
							    tokenized_datasets["train"]   ,		shuffle=__UpperCAmelCase   ,		collate_fn=__UpperCAmelCase   ,		batch_size=__UpperCAmelCase      )
							lowerCamelCase_			:    Dict   						=      DataLoader(
							    tokenized_datasets["validation"]   ,		shuffle=__UpperCAmelCase   ,		collate_fn=__UpperCAmelCase   ,		batch_size=__UpperCAmelCase      )

							return train_dataloader, eval_dataloader



def        __a							(  __UpperCAmelCase		:						Tuple   ,		__UpperCAmelCase		:						Optional[int]      ) ->					Tuple:
							"""simple docstring"""




							lowerCamelCase_			:    List[Any]   						=      Accelerator()

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							lowerCamelCase_			:    List[str]   						=      config['''lr''']
							lowerCamelCase_			:    List[str]   						=      int(config["num_epochs"]      )
							lowerCamelCase_			:    Union[str, Any]   						=      int(config["seed"]      )
							lowerCamelCase_			:    Optional[Any]   						=      int(config["batch_size"]      )
							lowerCamelCase_			:    List[Any]   						=      args.model_name_or_path

							set_seed(__UpperCAmelCase      )
							lowerCamelCase_			:    List[Any]   						=      get_dataloaders(__UpperCAmelCase   ,		__UpperCAmelCase   ,		__UpperCAmelCase   ,		args.n_train   ,		args.n_val      )

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							lowerCamelCase_			:    Optional[Any]   						=      AutoModelForSequenceClassification.from_pretrained(__UpperCAmelCase   ,		return_dict=__UpperCAmelCase      )

							# Instantiate optimizer
							lowerCamelCase_			:    List[Any]   						=      (
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							lowerCamelCase_			:    Tuple   						=      optimizer_cls(params=model.parameters()   ,		lr=__UpperCAmelCase      )

							if accelerator.state.deepspeed_plugin is not None:
														lowerCamelCase_			:    Tuple   						=      accelerator.state.deepspeed_plugin.deepspeed_config[
														    '''gradient_accumulation_steps'''
														]
							else:
														lowerCamelCase_			:    str   						=      1
							lowerCamelCase_			:    Optional[int]   						=      (len(__UpperCAmelCase      ) * num_epochs) // gradient_accumulation_steps

							# Instantiate scheduler
							if (
							    accelerator.state.deepspeed_plugin is None
							    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
							):
														lowerCamelCase_			:    Optional[int]   						=      get_linear_schedule_with_warmup(
														    optimizer=__UpperCAmelCase   ,		num_warmup_steps=0   ,		num_training_steps=__UpperCAmelCase   ,		)
							else:
														lowerCamelCase_			:    Optional[int]   						=      DummyScheduler(__UpperCAmelCase   ,		total_num_steps=__UpperCAmelCase   ,		warmup_num_steps=0      )

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.
							lowerCamelCase_			:    int   						=      accelerator.prepare(
							    __UpperCAmelCase   ,		__UpperCAmelCase   ,		__UpperCAmelCase   ,		__UpperCAmelCase   ,		__UpperCAmelCase      )

							# We need to keep track of how many total steps we have iterated over
							lowerCamelCase_			:    int   						=      0
							# We also need to keep track of the stating epoch so files are named properly
							lowerCamelCase_			:    Dict   						=      0

							# Now we train the model
							lowerCamelCase_			:    int   						=      {}
							for epoch in range(__UpperCAmelCase   ,		__UpperCAmelCase      ):
														with TorchTracemalloc() as tracemalloc:
																					model.train()
																					for step, batch in enumerate(__UpperCAmelCase      ):
																												lowerCamelCase_			:    Dict   						=      model(**__UpperCAmelCase      )
																												lowerCamelCase_			:    Any   						=      outputs.loss
																												lowerCamelCase_			:    Any   						=      loss / gradient_accumulation_steps
																												accelerator.backward(__UpperCAmelCase      )
																												if step % gradient_accumulation_steps == 0:
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()

																												overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
														accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin      )      )      )
														accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used      )      )
														accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked      )      )
														accelerator.print(
														    "Total Peak Memory consumed during the train (max): {}".format(
														        tracemalloc.peaked + bamb(tracemalloc.begin      )      )      )
														lowerCamelCase_			:    Union[str, Any]   						=      tracemalloc.peaked + bamb(tracemalloc.begin      )
														if args.peak_memory_upper_bound is not None:
																					assert (
																					    train_total_peak_memory[f"epoch-{epoch}"] <= args.peak_memory_upper_bound
																					), "Peak memory usage exceeded the upper bound"

							accelerator.wait_for_everyone()
							if accelerator.is_main_process:
														with open(os.path.join(args.output_dir   ,		"peak_memory_utilization.json"      )   ,		"w"      ) as f:
																					json.dump(__UpperCAmelCase   ,		__UpperCAmelCase      )



def        __a							(  ) ->					str:
							"""simple docstring"""




							lowerCamelCase_			:    List[Any]   						=      argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."      )
							parser.add_argument(
							    "--model_name_or_path"   ,		type=__UpperCAmelCase   ,		default="bert-base-cased"   ,		help="Path to pretrained model or model identifier from huggingface.co/models."   ,		required=__UpperCAmelCase   ,		)
							parser.add_argument(
							    "--output_dir"   ,		type=__UpperCAmelCase   ,		default="."   ,		help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."   ,		)
							parser.add_argument(
							    "--peak_memory_upper_bound"   ,		type=__UpperCAmelCase   ,		default=__UpperCAmelCase   ,		help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."   ,		)
							parser.add_argument(
							    "--n_train"   ,		type=__UpperCAmelCase   ,		default=320   ,		help="Number of training examples to use."   ,		)
							parser.add_argument(
							    "--n_val"   ,		type=__UpperCAmelCase   ,		default=160   ,		help="Number of validation examples to use."   ,		)
							parser.add_argument(
							    "--num_epochs"   ,		type=__UpperCAmelCase   ,		default=1   ,		help="Number of train epochs."   ,		)
							lowerCamelCase_			:    List[Any]   						=      parser.parse_args()
							lowerCamelCase_			:    Union[str, Any]   						=      {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
							training_function(__UpperCAmelCase   ,		__UpperCAmelCase      )


if __name__ == "__main__":
	main()






'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCAmelCase_  :							Optional[int]             =    logging.get_logger(__name__)

lowerCAmelCase_  :							List[str]             =    {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowerCAmelCase_  :							Any             =    [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]
def        UpperCAmelCase (    A	:					Dict					):
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {}
							with open(A       ,		'''r'''					) as file:
														for line_number, line in enumerate(A					):
																					SCREAMING_SNAKE_CASE       :   Dict         =      line.strip()
																					if line:
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      line.split()
																												SCREAMING_SNAKE_CASE       :   Any         =      line_number
																												SCREAMING_SNAKE_CASE       :   Any         =      words[0]
																												SCREAMING_SNAKE_CASE       :   Optional[Any]         =      value
							return result
def        UpperCAmelCase (    A	:					Union[str, Any]       ,		A	:					Tuple       ,		A	:					List[Any]       ,		A	:					str       ,		A	:					int					):
							for attribute in key.split('''.'''					):
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      getattr(A       ,		A					)

							SCREAMING_SNAKE_CASE       :   Any         =      None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(A					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      PARAM_MAPPING[full_name.split('''.'''					)[-1]]
																					SCREAMING_SNAKE_CASE       :   Tuple         =      '''param'''

							if weight_type is not None and weight_type != "param":
														SCREAMING_SNAKE_CASE       :   List[Any]         =      getattr(A       ,		A					).shape
							elif weight_type is not None and weight_type == "param":
														SCREAMING_SNAKE_CASE       :   List[str]         =      hf_pointer
														for attribute in hf_param_name.split('''.'''					):
																					SCREAMING_SNAKE_CASE       :   int         =      getattr(A       ,		A					)
														SCREAMING_SNAKE_CASE       :   int         =      shape_pointer.shape

														# let's reduce dimension
														SCREAMING_SNAKE_CASE       :   Dict         =      value[0]
							else:
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
														    F""" {value.shape} for {full_name}"""					)

							if weight_type == "weight":
														SCREAMING_SNAKE_CASE       :   str         =      value
							elif weight_type == "weight_g":
														SCREAMING_SNAKE_CASE       :   List[str]         =      value
							elif weight_type == "weight_v":
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      value
							elif weight_type == "bias":
														SCREAMING_SNAKE_CASE       :   Dict         =      value
							elif weight_type == "param":
														for attribute in hf_param_name.split('''.'''					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      getattr(A       ,		A					)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      value
							else:
														SCREAMING_SNAKE_CASE       :   Tuple         =      value

							logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""					)
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					List[str]       ,		A	:					Optional[int]       ,		A	:					Tuple       ,		A	:					Tuple					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(A					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      PARAM_MAPPING[full_name.split('''.'''					)[-1]]
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      '''param'''

							if weight_type is not None and weight_type != "param":
														SCREAMING_SNAKE_CASE       :   str         =      '''.'''.join([key, weight_type]					)
							elif weight_type is not None and weight_type == "param":
														SCREAMING_SNAKE_CASE       :   Tuple         =      '''.'''.join([key, hf_param_name]					)
							else:
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      key

							SCREAMING_SNAKE_CASE       :   int         =      value if '''lm_head''' in full_key else value[0]


lowerCAmelCase_  :							Union[str, Any]             =    {
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}
def        UpperCAmelCase (    A	:					List[Any]       ,		A	:					Tuple       ,		A	:					Dict=None       ,		A	:					Optional[int]=None					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      False
							for key, mapped_key in MAPPING.items():
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
														if key in name or key.split('''w2v_model.'''					)[-1] == name.split('''.'''					)[0]:
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      True
																					if "*" in mapped_key:
																												SCREAMING_SNAKE_CASE       :   int         =      name.split(A					)[0].split('''.'''					)[-2]
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      mapped_key.replace('''*'''       ,		A					)
																					if "weight_g" in name:
																												SCREAMING_SNAKE_CASE       :   List[Any]         =      '''weight_g'''
																					elif "weight_v" in name:
																												SCREAMING_SNAKE_CASE       :   int         =      '''weight_v'''
																					elif "bias" in name:
																												SCREAMING_SNAKE_CASE       :   str         =      '''bias'''
																					elif "weight" in name:
																												# TODO: don't match quantizer.weight_proj
																												SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''weight'''
																					else:
																												SCREAMING_SNAKE_CASE       :   Optional[int]         =      None
																					if hf_dict is not None:
																												rename_dict(A       ,		A       ,		A       ,		A       ,		A					)
																					else:
																												set_recursively(A       ,		A       ,		A       ,		A       ,		A					)
																					return is_used
							return is_used
def        UpperCAmelCase (    A	:					Optional[int]       ,		A	:					Any       ,		A	:					int					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      []
							SCREAMING_SNAKE_CASE       :   Any         =      fairseq_model.state_dict()

							SCREAMING_SNAKE_CASE       :   List[str]         =      hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														SCREAMING_SNAKE_CASE       :   str         =      False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A       ,		A       ,		A       ,		A       ,		hf_model.config.feat_extract_norm == '''group'''       ,		)
																					SCREAMING_SNAKE_CASE       :   List[str]         =      True
														else:
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_wavaveca_layer(A       ,		A       ,		A					)
														if not is_used:
																					unused_weights.append(A					)

							logger.warning(F"""Unused weights: {unused_weights}"""					)
def        UpperCAmelCase (    A	:					Tuple       ,		A	:					int       ,		A	:					Tuple       ,		A	:					int       ,		A	:					Union[str, Any]					):
							SCREAMING_SNAKE_CASE       :   str         =      full_name.split('''conv_layers.'''					)[-1]
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      name.split('''.'''					)
							SCREAMING_SNAKE_CASE       :   Tuple         =      int(items[0]					)
							SCREAMING_SNAKE_CASE       :   int         =      int(items[1]					)

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   str         =      value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
							else:
														unused_weights.append(A					)
@torch.no_grad()
def        UpperCAmelCase (    A	:					Any       ,		A	:					Union[str, Any]       ,		A	:					Optional[Any]=None       ,		A	:					Any=None       ,		A	:					Any=True       ,		A	:					str=False					):
							if config_path is not None:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      WavaVecaConfig.from_pretrained(A					)
							else:
														SCREAMING_SNAKE_CASE       :   Any         =      WavaVecaConfig()

							if is_seq_class:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      read_txt_into_dict(A					)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      idalabel
														SCREAMING_SNAKE_CASE       :   Tuple         =      WavaVecaForSequenceClassification(A					)
														SCREAMING_SNAKE_CASE       :   Tuple         =      WavaVecaFeatureExtractor(
														    feature_size=1       ,		sampling_rate=16000       ,		padding_value=0       ,		do_normalize=A       ,		return_attention_mask=A       ,		)
														feature_extractor.save_pretrained(A					)

							elif is_finetuned:
														if dict_path:
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      Dictionary.load(A					)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					SCREAMING_SNAKE_CASE       :   List[str]         =      target_dict.pad_index
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      target_dict.bos_index
																					SCREAMING_SNAKE_CASE       :   Dict         =      target_dict.eos_index
																					SCREAMING_SNAKE_CASE       :   Dict         =      len(target_dict.symbols					)
																					SCREAMING_SNAKE_CASE       :   str         =      os.path.join(A       ,		'''vocab.json'''					)
																					if not os.path.isdir(A					):
																												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(A					)					)
																												return
																					os.makedirs(A       ,		exist_ok=A					)
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					SCREAMING_SNAKE_CASE       :   str         =      0
																					SCREAMING_SNAKE_CASE       :   Tuple         =      1
																					with open(A       ,		'''w'''       ,		encoding='''utf-8'''					) as vocab_handle:
																												json.dump(A       ,		A					)
																					SCREAMING_SNAKE_CASE       :   Any         =      WavaVecaCTCTokenizer(
																					    A       ,		unk_token=target_dict.unk_word       ,		pad_token=target_dict.pad_word       ,		bos_token=target_dict.bos_word       ,		eos_token=target_dict.eos_word       ,		word_delimiter_token='''|'''       ,		do_lower_case=A       ,		)
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      True if config.feat_extract_norm == '''layer''' else False
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      WavaVecaFeatureExtractor(
																					    feature_size=1       ,		sampling_rate=16000       ,		padding_value=0       ,		do_normalize=A       ,		return_attention_mask=A       ,		)
																					SCREAMING_SNAKE_CASE       :   Any         =      WavaVecaProcessor(feature_extractor=A       ,		tokenizer=A					)
																					processor.save_pretrained(A					)

														SCREAMING_SNAKE_CASE       :   List[str]         =      WavaVecaForCTC(A					)
							else:
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      WavaVecaForPreTraining(A					)

							if is_finetuned or is_seq_class:
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[Any]         =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]       ,		arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''					)[:-1]					)}					)
							else:
														SCREAMING_SNAKE_CASE       :   str         =      argparse.Namespace(task='''audio_pretraining'''					)
														SCREAMING_SNAKE_CASE       :   Dict         =      fairseq.tasks.setup_task(A					)

														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   List[str]         =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,		task=A					)

							SCREAMING_SNAKE_CASE       :   List[Any]         =      model[0].eval()

							recursively_load_weights(A       ,		A       ,		not is_finetuned					)

							hf_wavavec.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							List[Any]             =    argparse.ArgumentParser()
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
							parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
							parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
							parser.add_argument(
							    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
							)
							parser.add_argument(
							    '--is_seq_class',
							    action='store_true',
							    help='Whether the model to convert is a fine-tuned sequence classification model or not',
							)
							lowerCAmelCase_  :							Union[str, Any]             =    parser.parse_args()

							lowerCAmelCase_  :							Dict             =    not args.not_finetuned and not args.is_seq_class
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.dict_path,
							    is_finetuned,
							    args.is_seq_class,
							)




